To address the pain points of traditional supply chain finance, such as static indicator limitations, information asymmetry, and low financing efficiency, this paper focuses on the deep integration of big data technology and supply chain finance for small and micro enterprises (SMEs). Combining the practices of manufacturing and service industries in Hebei Province, through literature research, case analysis, and field investigations, it systematically analyzes the technical principles of big data-empowered supply chain finance and the logical reconstruction of subject roles. A service quality control system of “common indicators + industry-specific indicators” and a full-process solution of “data collection and governance-risk quantification and early warning-collaborative service optimization” are constructed. Empirical results show that the system achieved a risk early warning accuracy rate of 89.2% for financial institutions, and the financing success rate of manufacturing SMEs in Hebei Province increased by 37% compared with the traditional model, effectively alleviating the financing dilemma. The industry-adapted model and cross-regional data sharing plan formed by the research provide a replicable “Hebei Experience” for the high-quality development of supply chain finance.